Overview

Dataset statistics

Number of variables23
Number of observations1330
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory386.4 KiB
Average record size in memory297.5 B

Variable types

Categorical4
Numeric19

Alerts

Country Name has a high cardinality: 266 distinct valuesHigh cardinality
Country Code has a high cardinality: 266 distinct valuesHigh cardinality
Current education expenditure, primary (% of total expenditure in primary public institutions) is highly overall correlated with Current education expenditure, secondary (% of total expenditure in secondary public institutions) and 4 other fieldsHigh correlation
Current education expenditure, secondary (% of total expenditure in secondary public institutions) is highly overall correlated with Current education expenditure, primary (% of total expenditure in primary public institutions) and 5 other fieldsHigh correlation
Current education expenditure, tertiary (% of total expenditure in tertiary public institutions) is highly overall correlated with Current education expenditure, primary (% of total expenditure in primary public institutions) and 5 other fieldsHigh correlation
Current education expenditure, total (% of total expenditure in public institutions) is highly overall correlated with Current education expenditure, primary (% of total expenditure in primary public institutions) and 5 other fieldsHigh correlation
Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative) is highly overall correlated with Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative) and 6 other fieldsHigh correlation
Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative) is highly overall correlated with Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative) and 6 other fieldsHigh correlation
Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative) is highly overall correlated with Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative) and 6 other fieldsHigh correlation
Educational attainment, at least completed primary, population 25+ years, total (%) is highly overall correlated with Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative) and 6 other fieldsHigh correlation
Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative) is highly overall correlated with Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative) and 6 other fieldsHigh correlation
Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative) is highly overall correlated with Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative) and 6 other fieldsHigh correlation
Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative) is highly overall correlated with Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative) and 6 other fieldsHigh correlation
Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative) is highly overall correlated with Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative) and 6 other fieldsHigh correlation
Expenditure on primary education (% of government expenditure on education) is highly overall correlated with Current education expenditure, primary (% of total expenditure in primary public institutions) and 5 other fieldsHigh correlation
Expenditure on secondary education (% of government expenditure on education) is highly overall correlated with Current education expenditure, primary (% of total expenditure in primary public institutions) and 5 other fieldsHigh correlation
Expenditure on tertiary education (% of government expenditure on education) is highly overall correlated with Current education expenditure, secondary (% of total expenditure in secondary public institutions) and 4 other fieldsHigh correlation
Government expenditure on education, total (% of GDP) is highly overall correlated with Government expenditure on education, total (% of government expenditure)High correlation
Government expenditure on education, total (% of government expenditure) is highly overall correlated with Government expenditure on education, total (% of GDP)High correlation
Year is highly overall correlated with AgeHigh correlation
Age is highly overall correlated with YearHigh correlation
Year is uniformly distributedUniform
Country Name is uniformly distributedUniform
Country Code is uniformly distributedUniform
Age is uniformly distributedUniform
GDP (current $) has 50 (3.8%) zerosZeros
Compulsory education, duration (years) has 115 (8.6%) zerosZeros
Current education expenditure, primary (% of total expenditure in primary public institutions) has 750 (56.4%) zerosZeros
Current education expenditure, secondary (% of total expenditure in secondary public institutions) has 725 (54.5%) zerosZeros
Current education expenditure, tertiary (% of total expenditure in tertiary public institutions) has 745 (56.0%) zerosZeros
Current education expenditure, total (% of total expenditure in public institutions) has 775 (58.3%) zerosZeros
Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative) has 850 (63.9%) zerosZeros
Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative) has 815 (61.3%) zerosZeros
Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative) has 940 (70.7%) zerosZeros
Educational attainment, at least completed primary, population 25+ years, total (%) has 880 (66.2%) zerosZeros
Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative) has 850 (63.9%) zerosZeros
Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative) has 805 (60.5%) zerosZeros
Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative) has 870 (65.4%) zerosZeros
Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative) has 940 (70.7%) zerosZeros
Expenditure on primary education (% of government expenditure on education) has 845 (63.5%) zerosZeros
Expenditure on secondary education (% of government expenditure on education) has 835 (62.8%) zerosZeros
Expenditure on tertiary education (% of government expenditure on education) has 800 (60.2%) zerosZeros
Government expenditure on education, total (% of GDP) has 245 (18.4%) zerosZeros
Government expenditure on education, total (% of government expenditure) has 245 (18.4%) zerosZeros

Reproduction

Analysis started2023-05-30 19:15:21.694905
Analysis finished2023-05-30 19:16:14.943326
Duration53.25 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Year
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size79.4 KiB
2016
266 
2017
266 
2018
266 
2019
266 
2020
266 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5320
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2017
3rd row2018
4th row2019
5th row2020

Common Values

ValueCountFrequency (%)
2016 266
20.0%
2017 266
20.0%
2018 266
20.0%
2019 266
20.0%
2020 266
20.0%

Length

2023-05-30T14:16:15.065691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T14:16:15.234758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 266
20.0%
2017 266
20.0%
2018 266
20.0%
2019 266
20.0%
2020 266
20.0%

Most occurring characters

ValueCountFrequency (%)
2 1596
30.0%
0 1596
30.0%
1 1064
20.0%
6 266
 
5.0%
7 266
 
5.0%
8 266
 
5.0%
9 266
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5320
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1596
30.0%
0 1596
30.0%
1 1064
20.0%
6 266
 
5.0%
7 266
 
5.0%
8 266
 
5.0%
9 266
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5320
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1596
30.0%
0 1596
30.0%
1 1064
20.0%
6 266
 
5.0%
7 266
 
5.0%
8 266
 
5.0%
9 266
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1596
30.0%
0 1596
30.0%
1 1064
20.0%
6 266
 
5.0%
7 266
 
5.0%
8 266
 
5.0%
9 266
 
5.0%

Country Name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct266
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size90.3 KiB
Afghanistan
 
5
Norway
 
5
Mozambique
 
5
Myanmar
 
5
Namibia
 
5
Other values (261)
1305 

Length

Max length52
Median length44
Mean length12.402256
Min length4

Characters and Unicode

Total characters16495
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan 5
 
0.4%
Norway 5
 
0.4%
Mozambique 5
 
0.4%
Myanmar 5
 
0.4%
Namibia 5
 
0.4%
Nauru 5
 
0.4%
Nepal 5
 
0.4%
Netherlands 5
 
0.4%
New Caledonia 5
 
0.4%
New Zealand 5
 
0.4%
Other values (256) 1280
96.2%

Length

2023-05-30T14:16:15.485358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100
 
4.0%
and 60
 
2.4%
income 55
 
2.2%
ida 50
 
2.0%
africa 45
 
1.8%
islands 45
 
1.8%
asia 40
 
1.6%
ibrd 40
 
1.6%
rep 35
 
1.4%
countries 35
 
1.4%
Other values (310) 2020
80.0%

Most occurring characters

ValueCountFrequency (%)
a 1935
 
11.7%
i 1295
 
7.9%
1195
 
7.2%
e 1150
 
7.0%
n 1110
 
6.7%
r 880
 
5.3%
o 785
 
4.8%
t 650
 
3.9%
s 600
 
3.6%
l 575
 
3.5%
Other values (50) 6320
38.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12475
75.6%
Uppercase Letter 2365
 
14.3%
Space Separator 1195
 
7.2%
Other Punctuation 265
 
1.6%
Open Punctuation 75
 
0.5%
Close Punctuation 75
 
0.5%
Dash Punctuation 45
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1935
15.5%
i 1295
10.4%
e 1150
 
9.2%
n 1110
 
8.9%
r 880
 
7.1%
o 785
 
6.3%
t 650
 
5.2%
s 600
 
4.8%
l 575
 
4.6%
d 565
 
4.5%
Other values (16) 2930
23.5%
Uppercase Letter
ValueCountFrequency (%)
A 260
 
11.0%
S 225
 
9.5%
I 195
 
8.2%
C 180
 
7.6%
B 160
 
6.8%
R 150
 
6.3%
M 145
 
6.1%
D 140
 
5.9%
E 120
 
5.1%
L 105
 
4.4%
Other values (15) 685
29.0%
Other Punctuation
ValueCountFrequency (%)
& 100
37.7%
. 85
32.1%
, 65
24.5%
' 10
 
3.8%
: 5
 
1.9%
Space Separator
ValueCountFrequency (%)
1195
100.0%
Open Punctuation
ValueCountFrequency (%)
( 75
100.0%
Close Punctuation
ValueCountFrequency (%)
) 75
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14840
90.0%
Common 1655
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1935
 
13.0%
i 1295
 
8.7%
e 1150
 
7.7%
n 1110
 
7.5%
r 880
 
5.9%
o 785
 
5.3%
t 650
 
4.4%
s 600
 
4.0%
l 575
 
3.9%
d 565
 
3.8%
Other values (41) 5295
35.7%
Common
ValueCountFrequency (%)
1195
72.2%
& 100
 
6.0%
. 85
 
5.1%
( 75
 
4.5%
) 75
 
4.5%
, 65
 
3.9%
- 45
 
2.7%
' 10
 
0.6%
: 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1935
 
11.7%
i 1295
 
7.9%
1195
 
7.2%
e 1150
 
7.0%
n 1110
 
6.7%
r 880
 
5.3%
o 785
 
4.8%
t 650
 
3.9%
s 600
 
3.6%
l 575
 
3.5%
Other values (50) 6320
38.3%

Country Code
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct266
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
AFG
 
5
NOR
 
5
MOZ
 
5
MMR
 
5
NAM
 
5
Other values (261)
1305 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3990
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFG
2nd rowAFG
3rd rowAFG
4th rowAFG
5th rowAFG

Common Values

ValueCountFrequency (%)
AFG 5
 
0.4%
NOR 5
 
0.4%
MOZ 5
 
0.4%
MMR 5
 
0.4%
NAM 5
 
0.4%
NRU 5
 
0.4%
NPL 5
 
0.4%
NLD 5
 
0.4%
NCL 5
 
0.4%
NZL 5
 
0.4%
Other values (256) 1280
96.2%

Length

2023-05-30T14:16:15.695389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afg 5
 
0.4%
aut 5
 
0.4%
bgr 5
 
0.4%
brn 5
 
0.4%
afw 5
 
0.4%
alb 5
 
0.4%
dza 5
 
0.4%
asm 5
 
0.4%
and 5
 
0.4%
ago 5
 
0.4%
Other values (256) 1280
96.2%

Most occurring characters

ValueCountFrequency (%)
A 325
 
8.1%
S 290
 
7.3%
M 275
 
6.9%
R 260
 
6.5%
N 245
 
6.1%
C 230
 
5.8%
L 220
 
5.5%
E 210
 
5.3%
T 205
 
5.1%
B 200
 
5.0%
Other values (16) 1530
38.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3990
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 325
 
8.1%
S 290
 
7.3%
M 275
 
6.9%
R 260
 
6.5%
N 245
 
6.1%
C 230
 
5.8%
L 220
 
5.5%
E 210
 
5.3%
T 205
 
5.1%
B 200
 
5.0%
Other values (16) 1530
38.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3990
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 325
 
8.1%
S 290
 
7.3%
M 275
 
6.9%
R 260
 
6.5%
N 245
 
6.1%
C 230
 
5.8%
L 220
 
5.5%
E 210
 
5.3%
T 205
 
5.1%
B 200
 
5.0%
Other values (16) 1530
38.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 325
 
8.1%
S 290
 
7.3%
M 275
 
6.9%
R 260
 
6.5%
N 245
 
6.1%
C 230
 
5.8%
L 220
 
5.5%
E 210
 
5.3%
T 205
 
5.1%
B 200
 
5.0%
Other values (16) 1530
38.3%

GDP (current $)
Real number (ℝ)

Distinct1272
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5833785 × 1012
Minimum0
Maximum8.7568054 × 1013
Zeros50
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:15.872773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.1697726 × 108
Q17.1841162 × 109
median4.8213165 × 1010
Q35.1339377 × 1011
95-th percentile1.9695651 × 1013
Maximum8.7568054 × 1013
Range8.7568054 × 1013
Interquartile range (IQR)5.0620965 × 1011

Descriptive statistics

Standard deviation8.9314293 × 1012
Coefficient of variation (CV)3.457267
Kurtosis34.591558
Mean2.5833785 × 1012
Median Absolute Deviation (MAD)4.721835 × 1010
Skewness5.3590948
Sum3.4358934 × 1015
Variance7.977043 × 1025
MonotonicityNot monotonic
2023-05-30T14:16:16.089411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
 
3.8%
1.705379935 × 10122
 
0.2%
3.436593923 × 10122
 
0.2%
1.563897898 × 10122
 
0.2%
1.754544849 × 10122
 
0.2%
1.80448233 × 10122
 
0.2%
3.597252024 × 10122
 
0.2%
3.34810885 × 10122
 
0.2%
3.386420412 × 10122
 
0.2%
2.926447614 × 10122
 
0.2%
Other values (1262) 1262
94.9%
ValueCountFrequency (%)
0 50
3.8%
36547799.58 1
 
0.1%
40619251.99 1
 
0.1%
42588164.97 1
 
0.1%
47271463.33 1
 
0.1%
48855550.2 1
 
0.1%
99723394.96 1
 
0.1%
109359680.2 1
 
0.1%
114626625.6 1
 
0.1%
118724073.8 1
 
0.1%
ValueCountFrequency (%)
8.756805441 × 10131
0.1%
8.626760063 × 10131
0.1%
8.474697912 × 10131
0.1%
8.119329166 × 10131
0.1%
7.630505887 × 10131
0.1%
5.50456476 × 10131
0.1%
5.455747758 × 10131
0.1%
5.398312976 × 10131
0.1%
5.346907564 × 10131
0.1%
5.346107754 × 10131
0.1%
Distinct19
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0466165
Minimum0
Maximum17
Zeros115
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:16.257962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median9
Q311
95-th percentile14
Maximum17
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.5075013
Coefficient of variation (CV)0.38771416
Kurtosis1.5026366
Mean9.0466165
Median Absolute Deviation (MAD)1
Skewness-1.1356558
Sum12032
Variance12.302566
MonotonicityNot monotonic
2023-05-30T14:16:16.402999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
9 309
23.2%
10 268
20.2%
12 134
10.1%
0 115
 
8.6%
11 88
 
6.6%
6 85
 
6.4%
8 80
 
6.0%
13 68
 
5.1%
7 55
 
4.1%
14 51
 
3.8%
Other values (9) 77
 
5.8%
ValueCountFrequency (%)
0 115
 
8.6%
5 15
 
1.1%
6 85
 
6.4%
6.5 5
 
0.4%
7 55
 
4.1%
8 80
 
6.0%
8.5 10
 
0.8%
9 309
23.2%
9.5 5
 
0.4%
10 268
20.2%
ValueCountFrequency (%)
17 4
 
0.3%
16 5
 
0.4%
15 25
 
1.9%
14 51
 
3.8%
13.5 5
 
0.4%
13 68
 
5.1%
12 134
10.1%
11 88
 
6.6%
10.5 3
 
0.2%
10 268
20.2%
Distinct438
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.015184
Minimum0
Maximum103.86063
Zeros750
Zeros (%)56.4%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:16.572926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q393.942703
95-th percentile99.997719
Maximum103.86063
Range103.86063
Interquartile range (IQR)93.942703

Descriptive statistics

Standard deviation46.789321
Coefficient of variation (CV)1.1407805
Kurtosis-1.9075937
Mean41.015184
Median Absolute Deviation (MAD)0
Skewness0.27432083
Sum54550.195
Variance2189.2406
MonotonicityNot monotonic
2023-05-30T14:16:16.757114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 750
56.4%
100 47
 
3.5%
95.5713501 5
 
0.4%
99.90708923 5
 
0.4%
80.51123047 5
 
0.4%
88.90242004 5
 
0.4%
99.21453094 5
 
0.4%
95.60489655 5
 
0.4%
98.76635742 5
 
0.4%
95.99887085 5
 
0.4%
Other values (428) 493
37.1%
ValueCountFrequency (%)
0 750
56.4%
73.56204987 1
 
0.1%
73.57845306 1
 
0.1%
75.01381683 1
 
0.1%
75.95323944 1
 
0.1%
77.27265167 1
 
0.1%
77.46252441 1
 
0.1%
78.1810379 1
 
0.1%
78.25955963 1
 
0.1%
79.76789093 1
 
0.1%
ValueCountFrequency (%)
103.8606262 1
0.1%
103.2098236 1
0.1%
101.9303131 1
0.1%
101.7783356 1
0.1%
101.5981598 1
0.1%
101.1627426 1
0.1%
100.9706446 1
0.1%
100.6938171 1
0.1%
100.6795654 1
0.1%
100.4516144 1
0.1%
Distinct456
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.33851
Minimum0
Maximum127.12766
Zeros725
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:16.908875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q394.196535
95-th percentile99.772446
Maximum127.12766
Range127.12766
Interquartile range (IQR)94.196535

Descriptive statistics

Standard deviation46.613652
Coefficient of variation (CV)1.1009752
Kurtosis-1.9242983
Mean42.33851
Median Absolute Deviation (MAD)0
Skewness0.21096346
Sum56310.219
Variance2172.8325
MonotonicityNot monotonic
2023-05-30T14:16:17.061956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 725
54.5%
100 38
 
2.9%
72.99436951 11
 
0.8%
96.40006256 5
 
0.4%
95.30285645 5
 
0.4%
98.16947174 5
 
0.4%
95.88538361 5
 
0.4%
96.60491943 5
 
0.4%
90.49446869 5
 
0.4%
91.17585754 5
 
0.4%
Other values (446) 521
39.2%
ValueCountFrequency (%)
0 725
54.5%
56.4992485 1
 
0.1%
57.16712952 1
 
0.1%
61.5271759 1
 
0.1%
64.99433136 1
 
0.1%
66.79857635 1
 
0.1%
68.90793228 1
 
0.1%
72.06997681 1
 
0.1%
72.4487381 5
 
0.4%
72.8215332 1
 
0.1%
ValueCountFrequency (%)
127.127655 1
0.1%
116.8700562 1
0.1%
110.0572128 1
0.1%
106.6124573 1
0.1%
105.0286064 1
0.1%
104.8462982 1
0.1%
104.5148621 1
0.1%
102.4231491 1
0.1%
102.000885 1
0.1%
101.657191 1
0.1%
Distinct450
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.5522
Minimum0
Maximum129.46688
Zeros745
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:17.200613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q392.245724
95-th percentile99.99299
Maximum129.46688
Range129.46688
Interquartile range (IQR)92.245724

Descriptive statistics

Standard deviation45.533964
Coefficient of variation (CV)1.1512372
Kurtosis-1.8269889
Mean39.5522
Median Absolute Deviation (MAD)0
Skewness0.3298087
Sum52604.426
Variance2073.3419
MonotonicityNot monotonic
2023-05-30T14:16:17.325538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 745
56.0%
100 48
 
3.6%
94.64640045 6
 
0.5%
84.21984863 5
 
0.4%
89.65959167 5
 
0.4%
90.10410309 5
 
0.4%
41.34651947 5
 
0.4%
99.95826721 5
 
0.4%
92.16117859 5
 
0.4%
91.34732819 5
 
0.4%
Other values (440) 496
37.3%
ValueCountFrequency (%)
0 745
56.0%
18.93601036 1
 
0.1%
24.39389038 1
 
0.1%
27.16605949 1
 
0.1%
31.41804504 1
 
0.1%
35.3086319 1
 
0.1%
36.62662125 1
 
0.1%
41.34651947 5
 
0.4%
45.1554842 1
 
0.1%
45.73155212 1
 
0.1%
ValueCountFrequency (%)
129.4668827 1
0.1%
127.4438477 1
0.1%
118.1587906 1
0.1%
108.8737335 1
0.1%
108.2059708 1
0.1%
104.6363602 1
0.1%
102.2573471 1
0.1%
102.255572 1
0.1%
101.8341646 1
0.1%
101.5976588 1
0.1%
Distinct430
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.627441
Minimum0
Maximum123.34792
Zeros775
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:17.478178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q392.626282
95-th percentile98.415104
Maximum123.34792
Range123.34792
Interquartile range (IQR)92.626282

Descriptive statistics

Standard deviation45.8491
Coefficient of variation (CV)1.1869567
Kurtosis-1.8445049
Mean38.627441
Median Absolute Deviation (MAD)0
Skewness0.35935983
Sum51374.496
Variance2102.14
MonotonicityNot monotonic
2023-05-30T14:16:17.847726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 775
58.3%
100 21
 
1.6%
95.5057373 5
 
0.4%
95.05690765 5
 
0.4%
95.80565643 5
 
0.4%
94.37013245 5
 
0.4%
88.18302917 5
 
0.4%
96.52876282 5
 
0.4%
96.93016815 5
 
0.4%
99.30043793 5
 
0.4%
Other values (420) 494
37.1%
ValueCountFrequency (%)
0 775
58.3%
66.11400859 1
 
0.1%
67.95620219 1
 
0.1%
71.01222229 1
 
0.1%
71.03983307 1
 
0.1%
72.01062393 1
 
0.1%
73.29293823 1
 
0.1%
73.76706696 5
 
0.4%
74.55966187 1
 
0.1%
74.65514374 1
 
0.1%
ValueCountFrequency (%)
123.3479156 1
0.1%
114.397377 1
0.1%
106.3638535 1
0.1%
106.013443 1
0.1%
105.7525558 1
0.1%
105.4468384 1
0.1%
103.5287857 1
0.1%
103.0929947 1
0.1%
101.3510462 1
0.1%
101.3505211 1
0.1%
Distinct337
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8115108
Minimum0
Maximum51.611785
Zeros850
Zeros (%)63.9%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:18.016217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.012961
95-th percentile31.108379
Maximum51.611785
Range51.611785
Interquartile range (IQR)12.012961

Descriptive statistics

Standard deviation10.930021
Coefficient of variation (CV)1.6046398
Kurtosis0.71505003
Mean6.8115108
Median Absolute Deviation (MAD)0
Skewness1.4042664
Sum9059.3094
Variance119.46537
MonotonicityNot monotonic
2023-05-30T14:16:18.184801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 850
63.9%
30.16695023 5
 
0.4%
18.17661095 5
 
0.4%
6.207419872 5
 
0.4%
4.322020054 5
 
0.4%
5.168910027 5
 
0.4%
30.84650993 5
 
0.4%
18.12311935 5
 
0.4%
3.477180004 5
 
0.4%
16.54598045 5
 
0.4%
Other values (327) 435
32.7%
ValueCountFrequency (%)
0 850
63.9%
0.9086199999 5
 
0.4%
1.559839964 1
 
0.1%
1.567257464 1
 
0.1%
1.582092464 1
 
0.1%
1.589509964 1
 
0.1%
1.827520013 5
 
0.4%
2.146270037 5
 
0.4%
2.248359919 1
 
0.1%
2.755000114 5
 
0.4%
ValueCountFrequency (%)
51.61178462 1
0.1%
47.29302979 1
0.1%
46.63145828 1
0.1%
46.56201172 1
0.1%
38.1034584 1
0.1%
37.52428818 1
0.1%
37.27647018 1
0.1%
37.0632267 1
0.1%
36.61830902 1
0.1%
36.50313854 1
0.1%
Distinct344
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.627496
Minimum0
Maximum100
Zeros815
Zeros (%)61.3%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:18.317743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q366.098536
95-th percentile97.800657
Maximum100
Range100
Interquartile range (IQR)66.098536

Descriptive statistics

Standard deviation38.104371
Coefficient of variation (CV)1.3792191
Kurtosis-1.0608171
Mean27.627496
Median Absolute Deviation (MAD)0
Skewness0.83459416
Sum36744.569
Variance1451.9431
MonotonicityNot monotonic
2023-05-30T14:16:18.450227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 815
61.3%
32.29368973 5
 
0.4%
73.3394165 5
 
0.4%
99.23863983 5
 
0.4%
99.45867157 5
 
0.4%
82.19438171 5
 
0.4%
86.16110229 5
 
0.4%
69.83145905 5
 
0.4%
12.62919998 5
 
0.4%
100 5
 
0.4%
Other values (334) 470
35.3%
ValueCountFrequency (%)
0 815
61.3%
7.794980049 1
 
0.1%
8.741460085 1
 
0.1%
9.275939941 5
 
0.4%
10.04080963 1
 
0.1%
10.63442016 1
 
0.1%
11.58090019 1
 
0.1%
12.62919998 5
 
0.4%
12.67080021 5
 
0.4%
13.40441036 5
 
0.4%
ValueCountFrequency (%)
100 5
0.4%
99.92174784 1
 
0.1%
99.91719055 1
 
0.1%
99.90807597 1
 
0.1%
99.90486908 1
 
0.1%
99.90351868 1
 
0.1%
99.82791901 1
 
0.1%
99.81135559 1
 
0.1%
99.80963135 1
 
0.1%
99.8079071 1
 
0.1%
Distinct263
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8826777
Minimum-1.8855948
Maximum79.020271
Zeros940
Zeros (%)70.7%
Negative1
Negative (%)0.1%
Memory size10.5 KiB
2023-05-30T14:16:18.567514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1.8855948
5-th percentile0
Q10
median0
Q314.422395
95-th percentile46.538183
Maximum79.020271
Range80.905866
Interquartile range (IQR)14.422395

Descriptive statistics

Standard deviation16.282928
Coefficient of variation (CV)1.8331103
Kurtosis2.6810571
Mean8.8826777
Median Absolute Deviation (MAD)0
Skewness1.8400971
Sum11813.961
Variance265.13376
MonotonicityNot monotonic
2023-05-30T14:16:18.696564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 940
70.7%
73.76670074 5
 
0.4%
54.74084854 5
 
0.4%
11.00074005 5
 
0.4%
31.10758972 5
 
0.4%
3.010210037 5
 
0.4%
60.41429901 5
 
0.4%
22.29335022 5
 
0.4%
20.24124908 5
 
0.4%
22.51861954 5
 
0.4%
Other values (253) 345
 
25.9%
ValueCountFrequency (%)
-1.885594845 1
 
0.1%
0 940
70.7%
2.611069679 1
 
0.1%
2.919329882 1
 
0.1%
3.010210037 5
 
0.4%
3.174329996 1
 
0.1%
3.42669493 1
 
0.1%
3.540910006 5
 
0.4%
4.14662981 1
 
0.1%
4.441425025 1
 
0.1%
ValueCountFrequency (%)
79.0202713 5
0.4%
73.76670074 5
0.4%
60.43206453 1
 
0.1%
60.41429901 5
0.4%
59.23622894 1
 
0.1%
58.99442291 1
 
0.1%
58.73769506 1
 
0.1%
58.03812027 1
 
0.1%
57.63240814 1
 
0.1%
57.24990082 1
 
0.1%
Distinct287
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.086369
Minimum0
Maximum100
Zeros880
Zeros (%)66.2%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:18.864813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q381.371742
95-th percentile99.708449
Maximum100
Range100
Interquartile range (IQR)81.371742

Descriptive statistics

Standard deviation41.381504
Coefficient of variation (CV)1.4733661
Kurtosis-1.1064027
Mean28.086369
Median Absolute Deviation (MAD)0
Skewness0.8861195
Sum37354.871
Variance1712.4289
MonotonicityNot monotonic
2023-05-30T14:16:18.984027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 880
66.2%
100 25
 
1.9%
14.70870018 5
 
0.4%
87.53704071 5
 
0.4%
63.55963135 5
 
0.4%
99.39974976 5
 
0.4%
71.22396088 5
 
0.4%
99.77649689 5
 
0.4%
99.78427124 5
 
0.4%
50.80500031 5
 
0.4%
Other values (277) 385
28.9%
ValueCountFrequency (%)
0 880
66.2%
12.82666969 1
 
0.1%
12.87027979 1
 
0.1%
12.95749998 1
 
0.1%
13.00111008 1
 
0.1%
14.70870018 5
 
0.4%
15.61793995 1
 
0.1%
17.99702072 5
 
0.4%
18.58679962 5
 
0.4%
21.92161942 5
 
0.4%
ValueCountFrequency (%)
100 25
1.9%
99.99059296 5
 
0.4%
99.94062042 5
 
0.4%
99.87837982 1
 
0.1%
99.86890411 1
 
0.1%
99.86148071 1
 
0.1%
99.85405731 1
 
0.1%
99.83921051 1
 
0.1%
99.80889893 1
 
0.1%
99.80570221 1
 
0.1%
Distinct333
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8565652
Minimum0
Maximum73.910278
Zeros850
Zeros (%)63.9%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:19.103652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316.245945
95-th percentile38.235135
Maximum73.910278
Range73.910278
Interquartile range (IQR)16.245945

Descriptive statistics

Standard deviation14.625072
Coefficient of variation (CV)1.6513255
Kurtosis2.385755
Mean8.8565652
Median Absolute Deviation (MAD)0
Skewness1.6867226
Sum11779.232
Variance213.89273
MonotonicityNot monotonic
2023-05-30T14:16:19.217576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 850
63.9%
6.167900085 5
 
0.4%
73.91027832 5
 
0.4%
16.56500053 5
 
0.4%
36.65642166 5
 
0.4%
23.06212044 5
 
0.4%
4.505449772 5
 
0.4%
5.168910027 5
 
0.4%
21.97097969 5
 
0.4%
40.81063843 5
 
0.4%
Other values (323) 435
32.7%
ValueCountFrequency (%)
0 850
63.9%
1.635550022 5
 
0.4%
1.974159956 5
 
0.4%
2.057670116 1
 
0.1%
2.136135101 1
 
0.1%
2.293065071 1
 
0.1%
2.371530056 1
 
0.1%
2.611069679 1
 
0.1%
2.675699949 5
 
0.4%
2.811320066 1
 
0.1%
ValueCountFrequency (%)
73.91027832 5
0.4%
71.40196482 1
 
0.1%
69.17845154 1
 
0.1%
64.73142497 1
 
0.1%
63.89017105 1
 
0.1%
62.50791168 1
 
0.1%
60.68957901 5
0.4%
57.40023422 1
 
0.1%
51.78467178 1
 
0.1%
51.76514816 1
 
0.1%
Distinct366
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.288363
Minimum0
Maximum98.04685
Zeros805
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:19.343453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q347.349689
95-th percentile86.275309
Maximum98.04685
Range98.04685
Interquartile range (IQR)47.349689

Descriptive statistics

Standard deviation31.751554
Coefficient of variation (CV)1.4245799
Kurtosis-0.60212377
Mean22.288363
Median Absolute Deviation (MAD)0
Skewness1.0016237
Sum29643.523
Variance1008.1612
MonotonicityNot monotonic
2023-05-30T14:16:19.454967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 805
60.5%
67.15647125 5
 
0.4%
97.3997879 5
 
0.4%
52.4560318 5
 
0.4%
70.77677155 5
 
0.4%
48.38576889 5
 
0.4%
7.3884902 5
 
0.4%
24.01808929 5
 
0.4%
44.55635834 5
 
0.4%
48.59965897 5
 
0.4%
Other values (356) 480
36.1%
ValueCountFrequency (%)
0 805
60.5%
5.408860207 1
 
0.1%
5.466910124 1
 
0.1%
5.583009958 1
 
0.1%
5.59581995 5
 
0.4%
5.641059875 1
 
0.1%
6.152450085 5
 
0.4%
6.184103012 1
 
0.1%
7.118770123 1
 
0.1%
7.3884902 5
 
0.4%
ValueCountFrequency (%)
98.04684957 1
 
0.1%
97.3997879 5
0.4%
96.67629242 1
 
0.1%
96.14924622 1
 
0.1%
94.73780823 5
0.4%
93.93517812 1
 
0.1%
93.45079041 1
 
0.1%
92.5988973 1
 
0.1%
92.56462097 1
 
0.1%
92.49629974 1
 
0.1%
Distinct309
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2840532
Minimum0
Maximum28.15081
Zeros870
Zeros (%)65.4%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:19.574738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5148799
95-th percentile13.870273
Maximum28.15081
Range28.15081
Interquartile range (IQR)1.5148799

Descriptive statistics

Standard deviation4.9022631
Coefficient of variation (CV)2.1462999
Kurtosis5.1463978
Mean2.2840532
Median Absolute Deviation (MAD)0
Skewness2.3931913
Sum3037.7908
Variance24.032183
MonotonicityNot monotonic
2023-05-30T14:16:19.678103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 870
65.4%
10.86900997 5
 
0.4%
1.514879942 5
 
0.4%
0.1320499927 5
 
0.4%
13.94503975 5
 
0.4%
1.197620034 5
 
0.4%
0.6929799914 5
 
0.4%
21.24132919 5
 
0.4%
1.076879978 5
 
0.4%
0.04569999874 5
 
0.4%
Other values (299) 415
31.2%
ValueCountFrequency (%)
0 870
65.4%
0.04569999874 5
 
0.4%
0.08917000145 5
 
0.4%
0.1320499927 5
 
0.4%
0.2081699967 5
 
0.4%
0.2606700063 5
 
0.4%
0.2966500223 1
 
0.1%
0.3041400015 5
 
0.4%
0.3396100998 1
 
0.1%
0.3908500075 1
 
0.1%
ValueCountFrequency (%)
28.15081024 1
 
0.1%
27.14703941 1
 
0.1%
26.95519002 1
 
0.1%
26.35737991 1
 
0.1%
25.7595698 1
 
0.1%
21.89410973 1
 
0.1%
21.24132919 5
0.4%
21.23752213 1
 
0.1%
21.214269 1
 
0.1%
20.58093452 1
 
0.1%
Distinct283
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17144511
Minimum0
Maximum4.0087466
Zeros940
Zeros (%)70.7%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:19.799233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.074110001
95-th percentile0.994927
Maximum4.0087466
Range4.0087466
Interquartile range (IQR)0.074110001

Descriptive statistics

Standard deviation0.41940515
Coefficient of variation (CV)2.446294
Kurtosis20.536405
Mean0.17144511
Median Absolute Deviation (MAD)0
Skewness3.8967002
Sum228.022
Variance0.17590068
MonotonicityNot monotonic
2023-05-30T14:16:19.924249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 940
70.7%
1.27531004 5
 
0.4%
0.1869399995 5
 
0.4%
0.05982000008 5
 
0.4%
0.9462000132 5
 
0.4%
0.02417000011 5
 
0.4%
0.3064900041 5
 
0.4%
0.9316099882 5
 
0.4%
0.07462000102 5
 
0.4%
0.008019999601 5
 
0.4%
Other values (273) 345
 
25.9%
ValueCountFrequency (%)
0 940
70.7%
0.008019999601 5
 
0.4%
0.01317000203 1
 
0.1%
0.01601999998 5
 
0.4%
0.02417000011 5
 
0.4%
0.03674999997 5
 
0.4%
0.03832000121 5
 
0.4%
0.04157999903 1
 
0.1%
0.0422774991 1
 
0.1%
0.04367249925 1
 
0.1%
ValueCountFrequency (%)
4.008746624 1
0.1%
3.582119942 1
0.1%
3.155493259 1
0.1%
2.982456525 1
0.1%
2.963419914 1
0.1%
2.934159994 1
0.1%
2.925346692 1
0.1%
2.906310081 1
0.1%
2.7557199 1
0.1%
2.53628993 1
0.1%
Distinct236
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.666402
Minimum0
Maximum85.22487
Zeros845
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:20.056352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324.96741
95-th percentile43.847547
Maximum85.22487
Range85.22487
Interquartile range (IQR)24.96741

Descriptive statistics

Standard deviation17.092715
Coefficient of variation (CV)1.465123
Kurtosis0.53743006
Mean11.666402
Median Absolute Deviation (MAD)0
Skewness1.2082
Sum15516.315
Variance292.16089
MonotonicityNot monotonic
2023-05-30T14:16:20.170812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 845
63.5%
23.81164 15
 
1.1%
22.21594 10
 
0.8%
24.96741 10
 
0.8%
26.93569 10
 
0.8%
22.83837 10
 
0.8%
35.87399 7
 
0.5%
34.8408 6
 
0.5%
30.57084 5
 
0.4%
23.878595 5
 
0.4%
Other values (226) 407
30.6%
ValueCountFrequency (%)
0 845
63.5%
0.56094 1
 
0.1%
0.60938 1
 
0.1%
0.65782 1
 
0.1%
0.70626 1
 
0.1%
0.7547 1
 
0.1%
12.49516 5
 
0.4%
12.70133 5
 
0.4%
12.92094 5
 
0.4%
13.23953 5
 
0.4%
ValueCountFrequency (%)
85.22487 1
 
0.1%
82.77339 1
 
0.1%
73.00851 1
 
0.1%
72.21043 1
 
0.1%
67.44895 1
 
0.1%
67.35112 1
 
0.1%
67.25329 1
 
0.1%
67.15546 1
 
0.1%
67.05763 1
 
0.1%
64.06315 5
0.4%
Distinct239
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.992055
Minimum0
Maximum68.56174
Zeros835
Zeros (%)62.8%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:20.284011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q331.46299
95-th percentile42.18401
Maximum68.56174
Range68.56174
Interquartile range (IQR)31.46299

Descriptive statistics

Standard deviation18.013997
Coefficient of variation (CV)1.3865395
Kurtosis-0.61366172
Mean12.992055
Median Absolute Deviation (MAD)0
Skewness0.90296624
Sum17279.433
Variance324.5041
MonotonicityNot monotonic
2023-05-30T14:16:20.500822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 835
62.8%
32.94466 15
 
1.1%
50.4102 11
 
0.8%
37.132345 10
 
0.8%
37.31704 10
 
0.8%
34.51418 10
 
0.8%
35.07155 5
 
0.4%
60.84435 5
 
0.4%
40.94874 5
 
0.4%
0.82897 5
 
0.4%
Other values (229) 419
31.5%
ValueCountFrequency (%)
0 835
62.8%
0.82897 5
 
0.4%
10.14186 1
 
0.1%
11.98943 1
 
0.1%
12.22592 1
 
0.1%
13.04418 1
 
0.1%
13.11073 1
 
0.1%
13.14359 1
 
0.1%
13.17728 1
 
0.1%
13.31038 1
 
0.1%
ValueCountFrequency (%)
68.56174 5
0.4%
64.32827 5
0.4%
63.72382 1
 
0.1%
63.08222 1
 
0.1%
62.72406 1
 
0.1%
62.22418 1
 
0.1%
61.7243 1
 
0.1%
60.84435 5
0.4%
53.44416 5
0.4%
52.82524 1
 
0.1%
Distinct273
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8951942
Minimum-25.46018
Maximum59.02014
Zeros800
Zeros (%)60.2%
Negative6
Negative (%)0.5%
Memory size10.5 KiB
2023-05-30T14:16:20.624653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-25.46018
5-th percentile0
Q10
median0
Q319.09854
95-th percentile26.82461
Maximum59.02014
Range84.48032
Interquartile range (IQR)19.09854

Descriptive statistics

Standard deviation11.027711
Coefficient of variation (CV)1.3967625
Kurtosis-0.050723696
Mean7.8951942
Median Absolute Deviation (MAD)0
Skewness0.98374869
Sum10500.608
Variance121.61041
MonotonicityNot monotonic
2023-05-30T14:16:20.743146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 800
60.2%
22.84284 15
 
1.1%
22.20075 11
 
0.8%
19.58165 10
 
0.8%
21.29603 10
 
0.8%
21.79022 10
 
0.8%
20.5208 10
 
0.8%
21.091 10
 
0.8%
19.09854 6
 
0.5%
20.36758 6
 
0.5%
Other values (263) 442
33.2%
ValueCountFrequency (%)
-25.46018 1
 
0.1%
-7.25041 1
 
0.1%
-6.48612 1
 
0.1%
-2.69738 1
 
0.1%
-2.08906 1
 
0.1%
-1.34126 1
 
0.1%
0 800
60.2%
0.00571 1
 
0.1%
0.00574 1
 
0.1%
0.00577 1
 
0.1%
ValueCountFrequency (%)
59.02014 1
0.1%
52.14544 1
0.1%
50.43606 1
0.1%
46.3553 1
0.1%
43.46023 1
0.1%
40.56516 1
0.1%
39.27335 1
0.1%
37.60704 1
0.1%
37.12156 1
0.1%
36.68386 2
0.2%

Government expenditure on education, total (% of GDP)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct911
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5307426
Minimum-0.53627014
Maximum13.51266
Zeros245
Zeros (%)18.4%
Negative1
Negative (%)0.1%
Memory size10.5 KiB
2023-05-30T14:16:20.846106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.53627014
5-th percentile0
Q12.310265
median3.7098849
Q34.8823262
95-th percentile7.3243499
Maximum13.51266
Range14.04893
Interquartile range (IQR)2.5720612

Descriptive statistics

Standard deviation2.288789
Coefficient of variation (CV)0.64824579
Kurtosis0.71062527
Mean3.5307426
Median Absolute Deviation (MAD)1.2452325
Skewness0.28633645
Sum4695.8877
Variance5.2385552
MonotonicityNot monotonic
2023-05-30T14:16:20.959370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 245
 
18.4%
4.091020107 9
 
0.7%
7.32434988 5
 
0.4%
3.450979948 5
 
0.4%
5.491350174 5
 
0.4%
1.324959993 5
 
0.4%
7.752439976 5
 
0.4%
3.905669928 5
 
0.4%
3.120650053 5
 
0.4%
4.29460001 5
 
0.4%
Other values (901) 1036
77.9%
ValueCountFrequency (%)
-0.5362701416 1
 
0.1%
0 245
18.4%
0.1274698973 1
 
0.1%
0.7912099361 1
 
0.1%
0.8215801716 1
 
0.1%
1.079903364 1
 
0.1%
1.152660012 1
 
0.1%
1.190230012 1
 
0.1%
1.22541666 1
 
0.1%
1.256580075 1
 
0.1%
ValueCountFrequency (%)
13.51266003 1
0.1%
13.1616834 1
0.1%
12.41641998 1
0.1%
12.39466 1
0.1%
11.96829033 1
0.1%
11.08613968 1
0.1%
11.02855968 1
0.1%
10.86316013 1
0.1%
10.65202999 1
0.1%
10.5601902 1
0.1%
Distinct926
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.553664
Minimum0
Maximum46.933926
Zeros245
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-30T14:16:21.082097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.5644217
median12.73095
Q315.835613
95-th percentile21.719329
Maximum46.933926
Range46.933926
Interquartile range (IQR)7.2711908

Descriptive statistics

Standard deviation6.9105651
Coefficient of variation (CV)0.59812759
Kurtosis0.27390962
Mean11.553664
Median Absolute Deviation (MAD)3.397665
Skewness-0.17117577
Sum15366.373
Variance47.75591
MonotonicityNot monotonic
2023-05-30T14:16:21.199282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 245
 
18.4%
12.73095036 6
 
0.5%
11.44194984 5
 
0.4%
9.385020256 5
 
0.4%
15.34716034 5
 
0.4%
7.773469925 5
 
0.4%
12.75354958 5
 
0.4%
12.40666962 5
 
0.4%
21.36655045 5
 
0.4%
14.15939045 5
 
0.4%
Other values (916) 1039
78.1%
ValueCountFrequency (%)
0 245
18.4%
0.8333600163 1
 
0.1%
0.8670300245 1
 
0.1%
0.8838650286 1
 
0.1%
0.9007000327 1
 
0.1%
1.073699951 1
 
0.1%
1.314839959 1
 
0.1%
1.70468998 1
 
0.1%
3.00248003 1
 
0.1%
3.529390097 1
 
0.1%
ValueCountFrequency (%)
46.93392563 1
0.1%
39.24699402 1
0.1%
35.00582886 1
0.1%
34.32725143 1
0.1%
32.72782898 1
0.1%
31.56006241 1
0.1%
30.15142059 1
0.1%
30 1
0.1%
28.28813934 1
0.1%
27.54687119 1
0.1%

Age
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size75.5 KiB
0
266 
1
266 
2
266 
3
266 
4
266 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1330
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
0 266
20.0%
1 266
20.0%
2 266
20.0%
3 266
20.0%
4 266
20.0%

Length

2023-05-30T14:16:21.298734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T14:16:21.410526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 266
20.0%
1 266
20.0%
2 266
20.0%
3 266
20.0%
4 266
20.0%

Most occurring characters

ValueCountFrequency (%)
0 266
20.0%
1 266
20.0%
2 266
20.0%
3 266
20.0%
4 266
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1330
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 266
20.0%
1 266
20.0%
2 266
20.0%
3 266
20.0%
4 266
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 266
20.0%
1 266
20.0%
2 266
20.0%
3 266
20.0%
4 266
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 266
20.0%
1 266
20.0%
2 266
20.0%
3 266
20.0%
4 266
20.0%

Interactions

2023-05-30T14:16:11.142775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:23.842958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:26.448407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:29.025343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:31.362099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:33.793876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:36.328889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:39.135175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:41.743903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:44.110847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:46.822434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:49.038554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:51.511126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:54.560739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:57.802598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:01.178293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:03.743221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:06.533792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:08.684337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:11.253497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:23.980067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:26.577309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:29.146073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:31.477404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:33.915116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:36.483393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:39.234587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:41.865751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:44.214630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:46.918651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:49.153655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:51.607575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:54.714143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:57.955293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:01.308076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:03.935952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:06.634686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:08.795886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:11.390677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:24.092608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:26.722800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:29.268001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:31.741178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:34.075615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:36.709594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:39.366861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:42.002364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:44.325704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:47.022706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:49.277713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:51.742344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:54.886669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:58.140000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:01.461062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:04.086243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:06.749680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:08.917883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:11.519870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:24.198121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:26.882600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:29.380107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:31.861765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:34.204337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:36.912708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:39.511303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:42.117840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-05-30T14:16:10.343827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:12.998936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:25.570682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:28.437312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:30.786699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:33.210742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:35.702284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:38.538877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:41.149894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:43.345858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:46.113406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:48.459333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:50.943763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:53.776782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:56.896346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:00.424204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:03.085186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:05.817957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:08.165138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:10.480000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:13.105306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:25.796206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:28.554857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:30.901244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:33.321780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:35.829266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:38.643713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:41.275817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:43.494165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:46.290778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:48.571801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:51.047046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:53.920850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:57.068175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:00.560388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:03.213207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:05.929445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:08.268409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:10.615071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:13.208269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:25.965659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:28.671324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:31.021807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:33.447438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:35.968210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:38.756116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:41.396921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:43.656482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:46.461065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:48.703026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:51.174715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:54.082310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:57.248019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:00.729068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:03.343838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:06.167126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:08.384168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:10.757413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:13.405566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:26.102801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:28.783982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:31.135004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:33.552180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:36.086232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:38.884655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:41.501168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:43.795581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:46.600280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:48.817704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:51.278457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:54.226418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:57.464105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:00.882068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:03.471878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:06.287189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:08.477669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:10.876149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:13.577639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:26.319456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:28.912230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:31.256547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:33.680544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:36.213515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:39.022576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:41.631058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:43.941300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:46.717994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:48.934758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:51.404471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:54.401730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:15:57.649056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:01.034802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:03.608710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:06.408413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:08.588362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T14:16:11.012094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-05-30T14:16:21.536095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
GDP (current $)Compulsory education, duration (years)Current education expenditure, primary (% of total expenditure in primary public institutions)Current education expenditure, secondary (% of total expenditure in secondary public institutions)Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)Current education expenditure, total (% of total expenditure in public institutions)Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)Educational attainment, at least completed primary, population 25+ years, total (%)Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)Expenditure on primary education (% of government expenditure on education)Expenditure on secondary education (% of government expenditure on education)Expenditure on tertiary education (% of government expenditure on education)Government expenditure on education, total (% of GDP)Government expenditure on education, total (% of government expenditure)YearAge
GDP (current $)1.0000.1910.0160.0690.1300.1180.1300.1230.1340.0890.1620.1550.1520.196-0.0280.0710.2020.1630.1460.0000.000
Compulsory education, duration (years)0.1911.0000.3470.3430.3110.3160.3020.3050.2660.2770.3180.3310.2960.2240.2020.2070.2600.1990.1090.0000.000
Current education expenditure, primary (% of total expenditure in primary public institutions)0.0160.3471.0000.9010.7080.7740.2970.3150.2710.2960.2750.3010.2730.2150.6440.5390.4680.3070.1580.0000.000
Current education expenditure, secondary (% of total expenditure in secondary public institutions)0.0690.3430.9011.0000.7230.7730.3450.3520.3590.3070.3290.3600.3470.2980.6160.5850.5360.2920.0980.0000.000
Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)0.1300.3110.7080.7231.0000.8140.3340.2990.2920.2680.3310.3490.2880.2760.5550.5290.5950.2970.1080.0000.000
Current education expenditure, total (% of total expenditure in public institutions)0.1180.3160.7740.7730.8141.0000.3300.3330.2720.3110.3060.3350.3100.2710.6080.5660.6170.3490.1460.0000.000
Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)0.1300.3020.2970.3450.3340.3301.0000.8710.7810.8010.8600.9170.8850.8130.1920.2390.2700.2000.0600.0000.000
Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)0.1230.3050.3150.3520.2990.3330.8711.0000.7490.8830.8520.9390.8290.7400.1910.2170.2870.2230.0980.0000.000
Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)0.1340.2660.2710.3590.2920.2720.7810.7491.0000.6550.7540.8070.7190.7040.2040.2710.3070.134-0.0300.0000.000
Educational attainment, at least completed primary, population 25+ years, total (%)0.0890.2770.2960.3070.2680.3110.8010.8830.6551.0000.7750.8260.7510.6620.1460.1950.2450.1760.0750.0000.000
Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)0.1620.3180.2750.3290.3310.3060.8600.8520.7540.7751.0000.9210.8260.7660.1970.2390.3190.2200.1230.0000.000
Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)0.1550.3310.3010.3600.3490.3350.9170.9390.8070.8260.9211.0000.8740.8010.2050.2580.3290.1870.0650.0000.000
Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)0.1520.2960.2730.3470.2880.3100.8850.8290.7190.7510.8260.8741.0000.8760.2390.2470.3210.2210.0640.0000.000
Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)0.1960.2240.2150.2980.2760.2710.8130.7400.7040.6620.7660.8010.8761.0000.1990.2120.2940.1920.0080.0000.000
Expenditure on primary education (% of government expenditure on education)-0.0280.2020.6440.6160.5550.6080.1920.1910.2040.1460.1970.2050.2390.1991.0000.8360.7060.2720.2040.0000.000
Expenditure on secondary education (% of government expenditure on education)0.0710.2070.5390.5850.5290.5660.2390.2170.2710.1950.2390.2580.2470.2120.8361.0000.7690.2570.1410.0000.000
Expenditure on tertiary education (% of government expenditure on education)0.2020.2600.4680.5360.5950.6170.2700.2870.3070.2450.3190.3290.3210.2940.7060.7691.0000.3080.1840.0000.000
Government expenditure on education, total (% of GDP)0.1630.1990.3070.2920.2970.3490.2000.2230.1340.1760.2200.1870.2210.1920.2720.2570.3081.0000.6160.0000.000
Government expenditure on education, total (% of government expenditure)0.1460.1090.1580.0980.1080.1460.0600.098-0.0300.0750.1230.0650.0640.0080.2040.1410.1840.6161.0000.0000.000
Year0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.000
Age0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.000

Missing values

2023-05-30T14:16:13.987604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-30T14:16:14.558160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

YearCountry NameCountry CodeGDP (current $)Compulsory education, duration (years)Current education expenditure, primary (% of total expenditure in primary public institutions)Current education expenditure, secondary (% of total expenditure in secondary public institutions)Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)Current education expenditure, total (% of total expenditure in public institutions)Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)Educational attainment, at least completed primary, population 25+ years, total (%)Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)Expenditure on primary education (% of government expenditure on education)Expenditure on secondary education (% of government expenditure on education)Expenditure on tertiary education (% of government expenditure on education)Government expenditure on education, total (% of GDP)Government expenditure on education, total (% of government expenditure)Age
02016AfghanistanAFG1.811656e+109.086.34567386.9396970.00.00.00.00.00.00.00.00.00.043.3020521.833890.03.51198013.0910000
12017AfghanistanAFG1.875347e+109.088.64099989.3640980.00.00.00.00.00.00.00.00.00.044.1906022.253690.03.37331012.0332001
22018AfghanistanAFG1.805323e+109.090.93632591.7884980.00.00.00.00.00.00.00.00.00.045.0791522.673490.03.19979011.6960602
32019AfghanistanAFG1.879945e+109.093.23165194.2128980.00.00.00.00.00.00.00.00.00.045.9677023.093290.03.21378011.3437703
42020AfghanistanAFG2.011614e+109.095.52697896.6372990.00.00.00.00.00.00.00.00.00.046.8562523.513090.03.11438010.2538604
52016Africa Eastern and SouthernAFE8.733549e+117.00.0000000.0000000.00.00.00.00.00.00.00.00.00.00.000000.000000.04.88108517.1988110
62017Africa Eastern and SouthernAFE9.853557e+117.00.0000000.0000000.00.00.00.00.00.00.00.00.00.00.000000.000000.05.02313016.7732001
72018Africa Eastern and SouthernAFE1.012853e+127.00.0000000.0000000.00.00.00.00.00.00.00.00.00.00.000000.000000.04.95163517.0124302
82019Africa Eastern and SouthernAFE1.009910e+127.00.0000000.0000000.00.00.00.00.00.00.00.00.00.00.000000.000000.04.71930015.3222953
92020Africa Eastern and SouthernAFE9.207923e+117.00.0000000.0000000.00.00.00.00.00.00.00.00.00.00.000000.000000.04.66537114.6967904
YearCountry NameCountry CodeGDP (current $)Compulsory education, duration (years)Current education expenditure, primary (% of total expenditure in primary public institutions)Current education expenditure, secondary (% of total expenditure in secondary public institutions)Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)Current education expenditure, total (% of total expenditure in public institutions)Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)Educational attainment, at least completed primary, population 25+ years, total (%)Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)Expenditure on primary education (% of government expenditure on education)Expenditure on secondary education (% of government expenditure on education)Expenditure on tertiary education (% of government expenditure on education)Government expenditure on education, total (% of GDP)Government expenditure on education, total (% of government expenditure)Age
13202016ZambiaZMB2.095841e+107.099.48397882.61277856.22547192.1531520.000000.0000000.00.0000000.000000.000000.00000.0000067.4489519.7251512.335143.74792015.4029700
13212017ZambiaZMB2.587360e+107.098.64071777.34137756.22547192.1531520.000000.0000000.00.0000000.000000.000000.00000.0000067.3511223.329988.916043.72964014.8818301
13222018ZambiaZMB2.631159e+107.097.79745572.06997756.22547192.1531520.000000.0000000.00.0000000.000000.000000.00000.0000067.2532926.934815.496944.73974017.0124302
13232019ZambiaZMB2.330867e+107.096.95419366.79857656.22547192.1531520.000000.0000000.00.0000000.000000.000000.00000.0000067.1554630.539642.077844.46518015.2918703
13242020ZambiaZMB1.811063e+107.096.11093161.52717656.22547192.1531520.000000.0000000.00.0000000.000000.000000.00000.0000067.0576334.14447-1.341264.70426712.3780204
13252016ZimbabweZWE2.054868e+107.00.0000000.0000000.0000000.0000003.2514164.9352260.082.4520269.4185312.264890.62150.036750.000000.000000.000005.47262023.5270810
13262017ZimbabweZWE1.758489e+107.00.0000000.0000000.0000000.0000003.2514164.9352260.082.4520269.4185312.264890.62150.036750.000000.000000.000005.38106020.8742011
13272018ZimbabweZWE1.811554e+107.00.0000000.0000000.0000000.0000003.2514164.9352260.082.4520269.4185312.264890.62150.036750.000000.000000.000003.58728019.0398412
13282019ZimbabweZWE1.928429e+107.00.0000000.0000000.0000000.0000003.2514164.9352260.082.4520269.4185312.264890.62150.036750.000000.000000.000002.64461016.7962213
13292020ZimbabweZWE1.805117e+107.00.0000000.0000000.0000000.0000003.2514164.9352260.082.4520269.4185312.264890.62150.036750.000000.000000.000001.70194014.5526014